Boundary matching and information theoretic scores have emerged as two important metrics to evaluate neuronal boundary maps [1]}, [2]}, [3]}. The most popular ones are similarity-oriented measures between two clusters for paired labels instead of pixel-wise errors. Boundary maps are transformed to segmentations by finding connected components. The rand index quantifies the similarity between the results of two clusters by taking the ratio of the sum of the total number of pairs of points in agreement and pairs of points in disagreements with respect to the total number of pairs between the two clusters. Another measure is based on what are known as the split and merge errors. The split error is computed by taking two randomly selected voxels belonging to the same segment in the ground truth and assessing them based on the joint probability of belonging to the same region in the segmentation result. The merge error is based on whether two voxels predicted as belonging to the same segment do actually belong together. The Rand F-score is then the weighted harmonic mean of the merge and split errors. Metrics like foreground-restricted rand scoring and foreground-restricted information theoretic scoring after border
thinning are the state-of-the-art metrics for neuronal boundary segmentation [4]}, [5]}, [6]}, [7]}, [8]}.
